COVID-19 Classification
Budget: $30 – $250 USD
# Classify counties or states in high/low or low/medium/high risk in terms of how affected they would be by a fourth wave.
# These results can be used to prepare the infrastructure and plan possible interventions (e.g., mask mandates, temporarily closing businesses and schools, etc.).
# Early interventions based on data might dampen a severe outbreak and therefore save lives and shorten the length of necessary closings.
# • Define your classes (e.g., more than x corona-related cases or fatalities per a population of 10000 per week).
# Explain why you defined the classes this way (maybe you want to look at the data first).
# • Combine files as needed to prepare the data set for classification. You will need a single table with a class attribute to learn a model.
# • Identify predictive features, create additional features, and deal with missing data (for classification models that cannot handle missing data).
# • Prepare the data for training, testing and hyper parameter tuning.
# • Create at least 3 different classification models (different techniques or using different class variables) using the training data.
# Discuss each model and the the advantages of each used classification method for your classification task.
# • Assess how well each model performs (use training/test data, cross validation, etc. as appropriate).
# • How useful is your model for your stakeholder? How would you assess the model's value if it was used.
# • How would your model be used in practice? What actions would be taken based on your model? How often would the model be updated? Etc.
# These results can be used to prepare the infrastructure and plan possible interventions (e.g., mask mandates, temporarily closing businesses and schools, etc.).
# Early interventions based on data might dampen a severe outbreak and therefore save lives and shorten the length of necessary closings.
# • Define your classes (e.g., more than x corona-related cases or fatalities per a population of 10000 per week).
# Explain why you defined the classes this way (maybe you want to look at the data first).
# • Combine files as needed to prepare the data set for classification. You will need a single table with a class attribute to learn a model.
# • Identify predictive features, create additional features, and deal with missing data (for classification models that cannot handle missing data).
# • Prepare the data for training, testing and hyper parameter tuning.
# • Create at least 3 different classification models (different techniques or using different class variables) using the training data.
# Discuss each model and the the advantages of each used classification method for your classification task.
# • Assess how well each model performs (use training/test data, cross validation, etc. as appropriate).
# • How useful is your model for your stakeholder? How would you assess the model's value if it was used.
# • How would your model be used in practice? What actions would be taken based on your model? How often would the model be updated? Etc.